Supply Chain Risk Management

Factors Affecting Logistic Performance: A Global Cross-Section Supply Chain Study

byMUHAMMAD ZAIN SIDDIQUIReg #: 8709Submitted to: Mr. Farhan Mehboob

A thesissubmitted in partial fulfillment of the requirementsfor the degree of Master of Business Administrationtothe Iqra University.

Karachi, Pakistan

Factors Affecting Logistic Performance: A Global Supply Chain Perspective 2

MAY, 2015

Abstract

The underlying objective and purpose of this thesis is to test a model that studies relationship between costs to export, cost to import, GDP, per capita income and IT on logistics performance. This research will assist the logistics industry for identifying the opportunities and challenges in terms of their trade logistics performance, what factors affect this benchmarking tool and what steps can the logistics industry take to improvise their performance. The data is selected for 41 countries worldwide on the basis of their land area from World Bank for the year of 2010.When the viability of the model was checked the results shown that all the independent variables contribute some exertions to affect the logistic performance of any country. The exports and imports of goods and services contribute to about 40% and 42% to the logistic performance to be precise. However, GDP, IT, and income per capita have an impact of about 16%, 8%, and 61% to the logistic performance respectively.However, for the countries having lower degree of logistic performance can improve their performance by focusing on their imports and exports of goods and services, and their per capita income which are the factors having enormous effect on the logistic performance of any country.

Table of ContentsAbstract2Chapter 1 Introduction11.0 Overview11.1 Background21.2 Problem Statement31.3 Purpose of Research31.4 Objectives of Research41.5 Research Questions41.6 Research Hypothesis51.7 Limitation of Study51.8 Scope6Chapter 2 Literature Review72.1 Theoretical Background82.2 Logistics82.3 Empirical Studies92.4 Logistics Framework102.5 GDP122.6 Cost to Export & Import122.7 Per Capita Income132.8 Information Technology132.9 Conceptual Framework14Chapter 3 Methodology153.1Research Purpose163.2Research Approach163.3 Research Design163.4 Secondary Data:173.5 Research Model173.6 Variables Description183.6.1 Dependent Variable:18LPI: Logistic Performance Index (overall).183.6.2 Independent Variable:19Trade Services & GDP19Cost to Export & Import19IT19References20

Factors Affecting Logistic Performance 7Chapter 1 Introduction1.0 OverviewLogistics form a significant base for success of organizations and businesses around the world. In terms of global comparison, the importance of logistic services largely depends on the nations economic power. For instance, the prospects of logistics services have been quite strong in Europe, Japan, and United States for a long time. There are certain factors that affect a dynamics of logistics in a country.First of all foreign trade, especially export is quite important to increase a countrys economic growth rate (Johnson, 2013). Moreover, export plays a key role for the countries to receive a greater share of the global market. Satisfactory and sustainability levels of countries export depend on exporting high value-added products and increasing the diversity of products and markets. Meanwhile, foreign trade transactions exhibit a complex view and have enhanced the importance of logistics. Logistics is considered as an important constituent in the field of service, manufacturing and agriculture industry. Moreover logistics has to be smoothly managed so that distribution and production functions can operate effectively.According to a research by Hollweg and Wong (2009) cost to import & import of goods and services; cost to export & export of goods and services and GDP are indirectly proportional to logistics performance index. On the other hand IT expense is directly proportional to logistics performance. In this regard, countries that work on controlling their cost of import, cost of export, GDP, IT enhances the quality of logistics and ensures competitiveness and eventually reach the top positions in the Logistics Performance.According to Christopher (2012) efficiency of logistics can be measured through the application of logistics performance index (LPI). This index primarily depends on the quality and competence of customs and border management, trade and transport infrastructure and logistics services. In this paper, it is worked on the model that studies relationship between costs to export, cost to import, GDP, per capita income and IT on logistics performance.This study investigates the affect of GDP, export and import of goods and services, cost to export and import, IT, and income per capita on logistic performance. The introductory chapter of this study will provide background information relevant to research questions, its contextual framework, and problem identification, purpose of study, research question, justification and limitation of this research.

1.1 BackgroundThe prospect of logistics performance starts with its definition. According to World Bank, the logistic performance of countries at the same level of per capita income with the best logistics performance experience additional growth of 1% in gross domestic product (GDP) and 2% in trade. So its essential to improve a countries logistics performance as it has significant valuable effects on the statistics of a countries economy. Additionally no matter if there is successful logistics or not the trade cycle is always present and it eventually relies on the pace and extent of government strategy and measures that will liberalize logistics supply (Havenga, 2011).Furthermore World Bank denotes LPI as an index that captures mainly the main features of the existing logistics environment. LPI is deliberated by the efforts of BRIC countries (Brazil, Russia, India and China); World Bank; and various other sophisticated emerging economies. Efficient supply chain and logistics of any country can become its competitive advantage over its competitor, so focus should be on improving the Logistics Performance Index of a country. LPI, as implied by the acronym, places great emphasis on performance, expressed through the reliability and predictability factor, unlike the conventional performance metrics, such as average delays and direct freight costs, or more generically expressed in terms of time and costs. World Bank representatives, experts in the field, and academics, came to the conclusion that, currently important indicators such as reliability, predictability and quality of service, along with transparency of processes, cannot be comprehended solely from costs and time information. The predictability and reliability of shipments, while more difficult to measure, are more important for firms and may have a more dramatic impact on their ability to compete (Arvis, et al 2007:4).

1.2 Problem StatementIn the year 2007, Singapore had the highest logistic performance index score of 4.19 with logistic competence of 4.21 which is the highest of all (World Bank). Whereas, when the data was extracted for the year 2010 Germany was the country where logistic performance index found to be 4.11 with logistic competence of 4.11 (World Bank). (Shown below in table 1.2.1)Table: 1.2.1CountryYearLPI RankLPI ScoreLogistics competence

Singapore200714.194.21

CountryYearLPI RankLPI ScoreLogistics competence

Germany201014.114.14

Korinek and Sourdin (2011) study based on low, middle and higher-income countries gives the idea that relationship between logistics and trade is directly proportional. Efficient logistics facilitates trade and play a crucial role of transporting goods over international border. On the other hand if logistics performance is inefficient, it will result in trade block up due to extra money and time needed (Korinek & Sourdin, 2011). As developed countries are shifting from traditional agriculture and manufacturing model to globalized trade they are increasingly interacting in international markets and need an efficient logistics services to gain competitive advantage. Therefore in this study we try to focus on logistics of developed and developing countries and finds out how quality and competency of logistics services is affected by country specific factors such as, GDP, export and import of goods and services, cost to export and import, IT, and income per capita.

1.3 Purpose of Research As mentioned above the quality logistics performance serves as a competitive advantage for countries. This research has tried to find factors which affect the Logistics dynamics and efficacy. For this research 41 countrys data will be assessed and influence of different variables will be examined on logistics. The independent variables which are selected for this research are also important and critical in todays world i.e. GDP, export and import of goods and services, cost to export and import, trade services, IT, and income per capita. This research can serve as a guideline for regulatory bodies to select strategic actions for improving their logistics. The fundamental idea of this research is to study the relationship between logistics performance and cost to export, cost to import, GDP, trade services, per capita income and IT (Arvis, et al 2007) on the basis of a model. This research will explore the relationship between dependent and independent variables on the basis of a model.

1.4 Objectives of Research The objective of this research is to assess the concept of logistics performance and various factors that affect its efficacy. The value of logistics performance is dependent on various factors and this paper explores the relationship among them.

1.5 Research Questions This study proposes to study the following questions:1. What is the impact of export and cost of goods and services on Logistics Performance?2. What is the impact of import and cost of goods and services on Logistics Performance?3. What is the impact of GDP on Logistics Performance?4. What is the impact of per capita income on Logistics Performance?5. What is the impact of Information Technology on Logistics Performance?

1.6 Research Hypothesis1. HO1: Export and cost of goods and services does not affect Logistics Performance?2. HO2: Import and cost of goods and services does not affect Logistics Performance?3. HO3: GDP does not affect Logistics Performance?4. HO4: Per capita income does not affect Logistics Performance?5. Ho5: Information Technology does not affect Logistics Performance?

1.7 Limitation of StudyThere are certain limitations in this research. 1. Limitation in terms of variables is that we have limited exposure of variables as we included the effect of only cost to export, cost to import, GDP, per capita income and IT on logistics performance, however dynamics of logistics are influenced by various other factors apart from these. Moreover, Researchers can include other factors to investigate logistics performance further.2. Another limitation is that this study is conducted on cross sectional data of 2010, so using panel or time series data can offer additional insights about the relationship of dependent and independent variables.3. The data gathered in this research is based on 41 countries that are selected on the basis of their size which can be further be taken on another countries as well.4. Quantitative model has been applied to study association between dependent and independent variables, so qualitative aspects can also be assessed to further gain insights into this topic.

1.8 Scope This research comprises on the data of 2010 for 41 countries. The data was taken from the website of World Bank. The countries were chosen on the basis of their size (area) and logistics data availability. Similarly, the research area can be more broaden by taking the data for more countries other than these 41 already selected on the base of their land area. Moreover, the research is based on the impacts of costs of exports and imports of goods and services, GDP, IT, and income per capita, where more other variables can be added to gauge the impact on logistic performance.

Chapter 2 Literature Review Factors Affecting Logistic Performance 24

2.1 Theoretical BackgroundThe theories of management that can be applied to the domain of logistics management are relationship orientation (Panayides & So, 2005), resource-based view (Rungtusanatham, Salvador, Forza & Choi, 2003), and competitive advantage (Sandberg & Abrahamsson, 2011). According to Panayides and So (2005) the idea of relationship orientation denotes proactively creating, developing and maintaining strong relationships with stakeholders that would ultimately provide benefit in the form of mutual exchange and profitable opportunities. Similarly in the domain of logistics management there are varies parties or stakeholders acting hand in hand to support the intricate operations of logistics, this there is a dire need for efficient relationship orientation.Moreover, the link between resource based view and logistics management is that capabilities and resources can only be obtained from a particular market to certain extent and after that there is a need to outsource the resources from other markets (ldrsson & Skjtt-Larsen, 2004). In this regard the concept of logistics becomes very important because it shows that its important for a country to improve it logistics services to achieve long-term mutual commitment (Rungtusanatham, Salvador, Forza & Choi, 2003). Similarly if a country wants to attain the competitive advantage it need to improve its logistics performance (Sandberg & Abrahamsson, 2011).

2.2 LogisticsLogistics by definition is considered a functional system that incorporates coordination and combination of operations of diverse transport modes as a primary pre-requisite for making sure that there is efficient service (Leal, 2012). In other words, logistics can be defined as a management framework for business planning for management of capital flows, information, service and material. Logistics function also incorporates the intricate control systems, IT and information that are needed in todays dynamic business environment. Furthermore logistics can also be defined as the replacement, distribution, maintenance and procurement of material and personnel. Logistics framework typically consists of physical distribution of services and goods, internal operations; physical distribution and internal operations; and physical supply of goods and services (Mentzer, Stank & Esper, 2008). Simultaneously, logistics framework can be seen as a structure that ensures that a country have the right type of service or product designated at the right order, place and time. However, our expectations for a firm or company are directly related to logistics.

2.3 Empirical Studies A report taken from the World Bank gives the idea that LPI is produced to close the knowledge gap related to logistics and to facilitate nations in developing reforms of to improve their competitive circumstances. The results of LPI ranking introduce some interesting findings; first, the higher the score in terms of LPI, the greater the countries role in logistics industry, and vice versa. On a second note, scoring low in LPI terms can be interpreted as being trapped in a vicious circle of overregulation, poor quality services, and under- investment (Arvis, et al 2007).In the study performed by Mohan in 2013, it was showed that the logistics management has effect on global competitiveness. Furthermore, the paper also examined the salient features of Indian logistics systems (Mohan 2013). The prospect of logistic performance index is built upon previous literature (Arvis, Mustra, Panzer, Ojala & Naula, 2007). Its focus however, is primarily on supply chain performance, and its indicators have been developed in such a way that, they complement the existing competitive indicators in the two fore-mentioned studies.According to Islam (2014) LPI consists of two main parts, namely International and Domestic LPI. The former has encompassed a range of metrics, they estimate as crucial in the current international trading environment, and conditions:1. Shipments timeliness in reaching target location 2. Effectiveness of clearance process monitored by customs agencies 3. Quality of transport infrastructure that is needed for efficient logistics4. Affordability and easiness of arranging shipments5. Ability to trace and track shipments6. Proficiency in local logistics industry (for instance, customs brokers and transport operators)7. Costs of domestic logistics (for instance, warehousing, terminal handling and local transportation)The second constituent of logistics performance is domestic logistics performance indicator that provides quantitative and qualitative assessments of a countrys logistics by professionals working inside it. According to Solakivi, Tyli, Engblom and Ojala (2011) domestic logistics tend to include comprehensive information on the cost data, performance time, institutions, and core logistics processes and logistics environment.

2.4 Logistics FrameworkThe significance of having an efficient logistics framework is currently acknowledged by decision makers worldwide. Private operators move commerce and trade are moved and within borders. Logistics performance actually measures the competence of these supply chain -logistics performance. The value of logistics performance depends on government policy that is formulated by regional economic groups and individual countries in development and regulation of services, infrastructure provision or trade facilitation all the way with the help of friendly border procedures that substantially facilitate in efficient performance of logistics (WTO 2014). According to Puertas, Mart and Garca (2013) the provision of International LPI is based on assessment of foreign operators and that consider the average of six components, namely, tracking and tracing; services quality; infrastructure; customs; timeliness; and international shipments.Fig 2.4 Input and Outcome LPI Indicators

Retrieved From: (WTO 2012)The components of supply chain delivery and logistics are selected on the basis of empirical and recent theoretical research and moreover on practical understanding of logistics professionals that are concerned with international freight forwarding (as shown in Fig 2.4). There have been four logistics performance surveys made so far accordingly in 2007, 2010, 2012 and 2014. On the basis of the worldwide survey of express carriers and global freight forwarders, the LPI is regarded as a benchmarking tool that evaluates performance of a country in terms of the efficacy of its logistics supply chain. This index allows comparisons of 160 countries, and therefore helps the countries in identifying opportunities and challenges in improving their logistics performance (WTO 2014). The value of logistics LPI ranges from 1 to 5, in which higher the number of index, the better comparative performance of the country (The World Bank 2014).

2.5 GDP According to Korinek and Sourdin (2011) trade logistics serves a very important job in facilitating trade services as it supports the transportation of international goods and services. On the other hand, if the logistics services are inefficient then they tend to impede trade services as there is imposition of extra money and time cost. Now a days developed nations are shifting from traditional agriculture and manufacturing and are progressing towards international vertical specialization, so due to this transition there is greater need for competent logistics services. Furthermore when quality of logistics services is enhanced, it improves a countrys competitive position by decreasing the overall costs that are involved in transporting good. So cost of imports and exports are closely link of logistics performance index (Korinek & Sourdin, 2011).

2.6 Cost to Export & ImportAccording to Arvis, Saslavsky, Ojala, Shepherd, Busch and Raj (2014) there is a strong association between logistics performance, consistency of supply chains and the service delivery certainty. So this denotes the idea that efficient logistics of a country directly supports its export function. Moreover, according to Puertas, Mart and Garca (2013) in terms of assessing a countrys export competitiveness; logistics performance has evolved as a decisive factor. According to World Economic Forum (2013) there is massive potential for enhancing global trade and export by reducing barriers of logistics and supply chain. Furthermore it is not easy for an exporter to export their goods and services at competitive prices if the logistics and transport sector is dysfunctional or inefficient. This means that if there is lack of certainty in logistics and transport, poor service and high prices, then it will translate in the form of isolating the country from world markets (Arvis et al., 2013).

2.7 Per Capita IncomeYildiz (2014) states that as domestic production per capita increases, so does the range of human activities, both for society as a whole and for individual lifestyles. Thus, each development proposal must be examined not only for its economic, environmental and social impacts, but also for its implications for transport. This denotes that as per capita income increases, it directly influences the increasing need for efficient transportation, which is turn leads to improvisation in logistics performance. 2.8 Information Technology According to OECD (2002) the use of ICT has improved the exchange of supply chain information, leading to the development of integrated production and logistics management systems and has thereby improved supply chain performance in many ways. This portray that due to the provision of improving Information technologies in any country, the logistics performance also improves significantly. This relationship in directly influenced by provision of Electronic Data Interchange (EDI) and ICT-supported information exchange systems. 2.9 Conceptual Framework The research is based on a conceptual framework that seeks to analyze the effect of cost to export (Arvis, Saslavsky, Ojala, Shepherd, Busch, & 2014; Hausman, Lee & Subramanian, 2005; Naud & Matthee, 2012; Turkson, 2011); cost to import (Arvis, Saslavsky, Ojala, Shepherd, Busch, & 2014; Hausman, Lee & Subramanian, 2005; Naud & Matthee, 2012; Turkson, 2011); GDP and Trade services (Diop, 2010; Havenga, 2011); and per capita income (Portugal-Perez & Wilson, 2012) on logistics performance.

Chapter 3 Methodology

The main goal of this research is to analyze the effect of cost to export, cost to import, import and export of goods and services, GDP, per capita income and IT on logistics performance.

3.1Research PurposeThe underlying objective of this thesis is to analyze the effect of cost to export, cost to import, GDP, per capita income and IT on logistics performance. This research will assist the logistics industry for identifying the opportunities and challenges in terms of their trade logistics performance, what factors affect this benchmarking tool and what steps can the logistics industry take to improvise their performance. Logistics industry is growing at an increasing pace but on the other hand, according to Business Recorder estimate, Pakistan is losing $2.6 billion annually because of inefficiencies in its logistics despite the significant growth in the efficiency of the road transport system (Mirza, 2013). So if logistics industry focuses on leveraging the key logistics performance indicators (LPI) then they can gain competitive advantage in long-run.

3.2Research ApproachThe approach of this research is based on multiple regressions that are focused on learning more about the relationship between several independent or predictor variables and a dependent or criterion variable. This thesis utilizes multivariate regression analysis that describes and evaluates the relationships between a given dependent variable and one or more independent variables.

3.3 Research DesignA general perception of quantitative research is to assess the association between dependent and independent research variables. In this cross-sectional research focus has been on statistical and numeric data collected from World Bank to study the effect of dependent variables on independent variables with the help of a proposed mode. Historical statistics has been used in this research. The research design connects the whole idea of research project together. The main approach is to conduct a quantitative analysis by gathering data from authentic sources. The criteria for selecting particular countries in data selection have been the size of the countries. The research data for this research was obtained from World Bank on the basis of size of country and the list of countries that are included in the data set for quantitative research is attached as Appendix 1.

3.4 Secondary Data:Data is collected from the World Banks website. In this, sample size of the study Top 41 countries on the basis of country sizes. In this study (OLS) ordinary least is used. This is a procedure to determine the relationship between the dependent and independent variable. In this study sample size top 41 countries based on year 2010. Ordinary least (OLS) is used in this study. It is a procedure to work out the relationship between the predictor (dependent) variable and predicted (independent) variables.

3.5 Research ModelFigure 3.7 Factors Affecting Logistic Performance

LPI = + 1(ImpCost) + 2(ImpG&S) + (Equation 1)LPI = + 1(ExpCost) + 2(ExpG&S) + (Equation 2)LPI = + 1(GDP) + (Equation 3)LPI = + 1(Income per capita) + (Equation 4)LPI = + 1(IT) + (Equation 5)

3.6 Variables Description3.6.1 Dependent Variable:LPI: Logistic Performance Index:Data relevant to the logistics performance is collected from World Bank. In logistics performance Index survey of 2009 more than 5,000 countries were included in evaluation that was carried out by almost 1,000 international freight forwarders. The research respondents evaluated eight markets on a scale of 1 to 5, on the basis of six dimensions. The dimensions are neighboring countries connecting country with global markets, landlocked countries, random selection, significant import and export markets of respondent's country.

3.6.2 Independent Variable:GDPAccording to Korinek and Sourdin (2011) if there is efficient trade logistics in any country then it will facilitate trade services of the country. Moreover the quality of logistics services serves an integral role in terms of supporting transportation of goods in international trade.

Cost to Export & ImportAccording to Puertas, Mart and Garca (2013) the notion of logistics performance has become a decisive factor in export competitiveness. At the same time, and as a result of the continuous enlargement processes it has undergone, the European Union is a very interesting case to study how the reforms that enhance logistics performance have affected export.

ITWhen the Information technology aspect of any country improves, it leads to significant improvement in dynamics of logistics as new technologies and advancements make logistics more affective (OECD, 2002).

CHAPTER 4

DATA ANALYSIS4.1 IntroductionThe main purpose of this research is to analyze the effect of Cost to Export, cost to import, GDP, per capita income and IT on logistics performance. Data analysis is considered as one of the most crucial phase of a quantitative research (Mangan, Lalwani & Gardner, 2004). The main purpose of data analysis is to identify the general theme of the collected data. The data analysis techniques being applied in this quantitative research are T-stats, Adjusted R-Squared, F-Statistics and Prob (F-Statistics). First of all in linear regression, the F-statistic is the test statistic for the analysis of variance (ANOVA) approach to test the significance of the model or the components in the model. The F-statistic in the linear model output display is the test statistic for testing the statistical significance of the model. The F-statistic values in the display are for assessing the significance of the terms or components in the model.Coefficient of determination (R-squared) indicates the proportionate amount of variation in the response variable y explained by the independent variables X in the linear regression model. The larger the R-squared is the more variability is explained by the linear regression model. The analysis has been conducted for the data of the year 2010.

Table 4.1Highest and Lowest Countries (three) with rest to dependent variable (LPI) (2010)VariablesExportsImportsGDPIncome per capitaLPI

UnitUS$ (Mn)US$ (Mn)US$ (Mn)US$0-5

Top 3 LPI Countries

Germany1,558,7601,373,9133,284,47440,1644.11

Sweden223,444196,440462,90349,3604.08

Japan871,533796,6745,488,41643,0633.97

Bottom 3 LPI Countries

Iraq54,59947,19781,1122,5322.11

Angola51,45235,42182,4714,3222.25

Algeria60,65650,792161,9794,5672.36

According to the results of table 4.1, the highest ranking country in terms of respective LPI is Germany with LPI of 4.11. However, Germany has less per capita income and GP in comparison to Sweden and Japan. The fact that Germany ranks highest in LPI comes from Germanys lead in net exports and imports market. Secondly, Sweden takes the 2n highest place in terms of LPI, due to its highest per capita income. On the other hand, Swedens LPI is lower than that of Germany due to lesser GDP, exports and imports. The third highest place is taken by Japan, with an LPI of 3.97. Japan is leading due to its highest GDP and relatively better import and export outlook in comparison to Sweden.Moreover, the table 4.1 shows that highest and lowest three countries in terms of their respective logistics performance for 2010. According to this table, Iraq has the lowest LPI of 2.11, while on the other hand Iraqs income per capita and GDP is also lowest at $2,532 million and 81,112 million respectively. In terms of exports and imports, Iraq ranks better than Angola with net exports and imports of $54,599 million and $47,197 accordingly. On the other hand Angolas LPI is 2nd lowest at around 2.25, having better per capita income and GDP than Iraq at $4,322 million and 82,471 million respectively; net exports of 51,452 an imports worth of 35,421. All values of Angolas variables are less than those of Iraq other than per capita income and GDP which shows that the logistic infrastructure of Angola not fully utilized at its full potential however it has a relatively good GDP an economic outlook. To add on, Algeria has the 3rd lowest LPI of 2.36 as it has relatively better export and import outlook; more profound GDP and per capita income, in comparison with Iraq and Algeria.

being underutilized and is not being used to its full potential. Japan on the other hand has an LPI little less than Singapore which is 3.8 but her AIRTRANS and RSGAS is almost double with respect to Singapore. This shows that the Japans infrastructure has more capacity to handle transport related activities than Singapore. Japans FPI is almost the same of Singapore and has a CPI of around 91. Oman has the 3rd highest LPI having a very low AIRTRANS as compared to Singapore and Japan but her RSGAS is almost double than the RSGAS of Japan which is a little above than 671. Oman Logistics mostly rely on road sector transportation and very little on air transportation. Oman crop and food production is although higher than Japan but Omans air transport is way lower than that of Japan.

4.2 Statistical Result Table 4.2.1 Ordering Least SquareDependent Variable: LPI5

Method: Least Squares

Date: 04/14/15 Time: 19:55

Sample: 1 41

Included observations: 41

VariableCoefficientStd. Errort-StatisticProb.

COSTEXP-0.0002959.95E-05-2.9629130.0052

EXPGS5.81E-131.53E-133.7840980.0005

C3.3640840.15559421.620930.0000

R-squared0.430501

Adjusted R-squared0.400527

Durbin-Watson stat1.998539

F-statistic14.36263

Prob(F-statistic)0.000023

Prob. F(5,35)0.6729

The above table consists of the cost of import that is our independent variable and the logistics performance index is the dependent variable in this statistical test. Statistical analysis of the model reveals that the adjusted R- square value is 0.400 which clarifies that logistic performance can be projected by 40% of the variance in exports of goods and services and its cost.The measure of probability of F-statistics reveals that whether the model of is important or not. The probability of f-statistics must be greater than 0.1, which generally indicates that logistics performance is overall irrelevant. In our model the value of f-statistics is 0.000023 and this highlights the fact that the independent variable of our research (LPI) is very much significant. However, the value of Heteroskedasticity is 0.672 which shows that the hetro factor is not present in the results of this model.

Table 4.2.2 Ordering Least SquareDependent Variable: LPI5

Method: Least Squares

Date: 04/14/15 Time: 19:57

Sample: 1 41

Included observations: 41

VariableCoefficientStd. Errort-StatisticProb.

COSTIMP-0.0002948.91E-05-3.2947360.0021

IMPGS5.48E-131.42E-133.8498630.0004

C3.4091100.15054422.645340.0000

R-squared0.448039

Adjusted R-squared0.418988

F-statistic15.42270

Prob(F-statistic)0.000012

Durbin-Watson stat2.085751

Prob. F(5,35) 0.5754

In the above table independent variables are imports of goods and services and its cost and the dependent variable is logistics performance index. The above model shows that the adjusted R- square value is 0.418 that shows that imports of goods and services and its cost can be projected by 41% variance in the Logistics performance.The Probability of F-statistics reveals that the model of summary is significant or insignificant. Rule of thumb is that if the value of probability of f-statistics is greater than 0.10 then it indicates that the imports of goods and services and the cost are insignificant. In our model the value of f-statistics is 0. 000012 which indicates that independent variable is significant in forecasting the varying value of dependent variable.The coefficient value of imports of goods and services and its cost is -0.000294, which means that is there is (one) unit addition in cost to import & import of goods and services will result into 0.024% reduction in logistics performance.

Table 4.2.3 Ordering Least SquareDependent Variable: LPI5

Method: Least Squares

Date: 04/14/15 Time: 20:00

Sample: 1 41

Included observations: 41

VariableCoefficientStd. Errort-StatisticProb.

GDP$9.47E-143.19E-142.9701830.0051

C3.0612290.08873634.498140.0000

R-squared0.184476

Adjusted R-squared0.163565

Durbin-Watson stat2.033192

F-statistic8.821987

Prob(F-statistic)0.005072

Prob. F(2,38) 0.8754

The above table consists of the gross domestic product as the independent variable and the dependent variable in this statistical test is logistic performance. Our statistical model reveals that the adjusted R- square value is 0.163 which depict that Gross Domestic Product and Trade Services can be projected by 16.3% of the variance in the logistics performance.Probability of f-statistics shows that the model of summary is significant or insignificant. In our model the value of f-statistics is 0.005 which indicates that independent variable is significant taking into consideration good to forecast the varying value of dependent variable i.e. logistics performance.However, the value of Heteroskedasticity is 0.875 which shows that the hetro factor is not present in the results of this model.

Table 4.2.4 Ordering Least SquareHeteroskedasticity-corrected, using observations 1-41Dependent variable: LPI5

CoefficientStd. Errort-ratiop-value

const2.812520.14032620.0428